Data Literacy in Practice: A complete guide to data literacy and making smarter decisions with data through intelligent actions
- Length: 396 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2022-11-30
- ISBN-10: 1803246758
- ISBN-13: 9781803246758
- Sales Rank: #345573 (See Top 100 Books)
Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data
Key Features
- Get a solid grasp of data literacy fundamentals to support your next steps in your career
- Learn how to work with data and extract meaningful insights to take the right actions
- Apply your knowledge to real-world business intelligence projects
Book Description
Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin―data literacy.
This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights.
You’ll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights.
By the end of the book, you’ll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making.
What you will learn
- Start your data literacy journey with simple and actionable steps
- Apply the four-pillar model for organizations to transform data into insights
- Discover which skills you need to work confidently with data
- Visualize data and create compelling visual data stories
- Measure, improve, and leverage your data to meet organizational goals
- Master the process of drawing insights, ask critical questions and action your insights
- Discover the right steps to take when you analyze insights
Who this book is for
This book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you’re looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you’ll find this book useful.
Data Literacy in Practice Contributors About the authors About the reviewers Preface Who this book is for What this book covers Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book Part 1: Understanding the Data Literacy Concepts Chapter 1: The Beginning – The Flow of Data Understanding data in our daily lives Analyzing data Searching and finding information An introduction to data literacy The COVID-19 pandemic The organizational data flow The DIDM journey The success story of The Oakland A’s Summary Chapter 2: Unfolding Your Data Journey Growing toward data and analytics maturity Descriptive analyses and the data path to maturity Understanding descriptive analysis Identifying qualitative or quantitative data Understanding diagnostic analysis Understanding predictive analytics Understanding prescriptive analytics AI Can data save lives? A success story Summary Chapter 3: Understanding the Four-Pillar Model Gaining an understanding of the various aspects of data literacy Introducing the four fundamental pillars Becoming acquainted with organizational data literacy Discussing the significance of data management Defining a data and analytics approach The rapid growth of our data world Tools The rise of ML and AI Moving to the cloud Data literacy is a key aspect of data and analytics Understanding the education pillar Mixing the pillars Summary Chapter 4: Implementing Organizational Data Literacy Implementing organizational data literacy Planning the data literacy vision Communicating the data literacy vision Focusing on desired outcomes Adopting a systemic perspective Getting everyone involved in the whole process Developing a data-literate culture Managing change Driving resilience Managing the organization’s skills and knowledge Creating a data literacy educational program Identifying employee roles Learning levels Covering all moments of need Learning methodologies Including all knowledge types Learning elements Organizing content Searching for content Measuring success Celebrating successes Summary Further reading Chapter 5: Managing Your Data Environment Introducing data management Understanding your data quality Intermezzo – Starting to improve data quality in a small-scaled healthcare environment Delivering a data management future Data strategy Taking care of your data strategy Creating a data vision Identifying your data Discovering where your data is stored Retrieving your data Combining and enriching data Setting the standard Processes Control IT Summary Part 2: Understanding How to Measure the Why, What, and How Chapter 6: Aligning with Organizational Goals Understanding the types of indicators Identifying KPIs Characteristics of KPIs Leading and lagging indicators Reviewing for unintended consequences Applying Goodhart’s law to KPIs Defining what to track Activity system maps Logic models Summary References Chapter 7: Designing Dashboards and Reports The importance of visualizing data Deceiving with bad visualizations Using our eyes and the usage of colors Introducing the DAR(S) principle Defining your dashboard Choosing the right visualization Understanding some basic visualizations Bar chart (or column chart or bar graph) Line chart Pie chart Heatmap Radar chart Geospatial charts KPIs in various ways Tables Presenting some advanced visualizations Bullet charts Addressing contextual analysis Summary Chapter 8: Questioning the Data Being curious and critical by asking questions Starting with the problem – not the data Identifying the right key performance indicators (KPIs) ahead of time Questioning not just the data, but also assumptions Using a questioning framework Questioning based on the decision-making stage Questioning data and information Questioning analytic interpretations and insights Summary References Chapter 9: Handling Data Responsibly Introducing the potential risks of data and analytics Identifying data security concerns Intermezzo – a data leak at an airplane carrier Identifying data privacy concerns Identifying data ethical concerns Intermezzo – tax office profiles ethnically Summary Part 3: Understanding the Change and How to Assess Activities Chapter 10: Turning Insights into Decisions Data-informed decision-making process Ask – Identifying problems and interpreting requirements Acquire – Understanding, acquiring, and preparing relevant data Analyze – Transforming data into insights Apply – Validating the insights Act – Transforming insights into decisions Announce – Communicating decisions with data Assess – Evaluating outcomes of a decision Making a data-Informed decision in action Using a data-informed decision checklist Why data-informed over data-driven? Storytelling Why is communicating with data so hard? Three key elements of communication Why include a narrative? The process Summary Further reading Chapter 11: Defining a Data Literacy Competency Framework Data literacy competency framework Identifying problems and interpreting requirements Understanding, acquiring, and preparing relevant data Turning data into insights Validating the insights Transforming insights into decisions Communicating decisions with data Evaluating the outcome of a decision Understanding data Data literacy skills Identifying data literacy technical skills Data literacy soft skills Data literacy mindsets Summary References Chapter 12: Assessing Your Data Literacy Maturity Assessing individual data literacy Assessing organizational data literacy Basic organizational data literacy assessment Robust organizational data literacy maturity assessment Summary Chapter 13: Managing Data and Analytics Projects Discovering why data and analytics projects fail Understanding four typical data and analytics project characteristics Understanding data and analytics project blockers Pitfalls in data and analytics projects Lack of expertise The technical architecture Time and money Unfolding the data and analytics project approach Unfolding the data and analytics project framework Intermezzo 2 – successfully managing a data and analytics project Mitigating typical data and analytics project risks Project risks Technical risks Cultural risks Content risks Determining roles in data and analytics projects (and teams) Managing data and analytics projects Writing a successful data and analytics business case A chapter layout for your business case Finding financial justification for your project Argumentation for one-time project costs Annual recurring costs Argumentation for annual recurring costs The quantitative benefits ROI Conclusion and advice Summary Chapter 14: Appendix A – Templates Project intake form STARR TEMPLATE Layout for a business case Layout for a business case scenario description A business case financial analysis Layout for a risk assessment Layout for a summary business case Layout information and measure plan Layout for a KPI description Table with the Inmon groups and a description of their roles Chapter 15: Appendix B – References Inspirational books Online articles and blogs Dutch articles and blogs Online tools Online sites Index Why subscribe? 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